Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification
IntroductionThis study aims to explore the relationship between healthcare and future education among the rural low-income population, using J City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide pol...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-11-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1384474/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846163495440089088 |
|---|---|
| author | Xiaoyan Peng Yanzhao Zeng Yanrui Chen Huaxing Wang Huaxing Wang |
| author_facet | Xiaoyan Peng Yanzhao Zeng Yanrui Chen Huaxing Wang Huaxing Wang |
| author_sort | Xiaoyan Peng |
| collection | DOAJ |
| description | IntroductionThis study aims to explore the relationship between healthcare and future education among the rural low-income population, using J City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic.MethodsUtilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals. Additionally, a questionnaire survey methodology was employed to separately investigate healthcare and future education dimensions among the identified population.ResultsThe proposed model achieved a population identification accuracy of 91.93%, surpassing other baseline neural network algorithms by at least 2.65%. Survey results indicated low satisfaction levels in healthcare areas, including medical resource distribution, medication costs, and access to basic medical facilities, with satisfaction rates below 50%. Regarding future education, issues such as tuition burdens, educational opportunity disparities, and accessibility challenges highlighted the concerns of rural low-income families.DiscussionThe high accuracy of the model demonstrates its potential for precise identification and classification of low-income populations. Insights derived from healthcare and education surveys reveal systemic issues affecting satisfaction and accessibility. This research thus provides a valuable foundation for future studies and policy development targeting rural low-income populations in healthcare and education. |
| format | Article |
| id | doaj-art-8d66ba4a6a6940bdb8d4706778d7a327 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-8d66ba4a6a6940bdb8d4706778d7a3272024-11-19T06:15:35ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-11-011210.3389/fpubh.2024.13844741384474Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identificationXiaoyan Peng0Yanzhao Zeng1Yanrui Chen2Huaxing Wang3Huaxing Wang4School of Government, Sun Yat-sen University, Guangzhou, ChinaSchool of Economics and Statistics, Guangzhou University, Guangzhou, ChinaSchool of Public Administration, Guangzhou University, Guangzhou, ChinaInstitute of Urban Development and Strategy, Law School, Research Center for Digitalization and Rural Development, Hangzhou City University, Hangzhou, ChinaSchool of Economics, Zhejiang University, Hangzhou, ChinaIntroductionThis study aims to explore the relationship between healthcare and future education among the rural low-income population, using J City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic.MethodsUtilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals. Additionally, a questionnaire survey methodology was employed to separately investigate healthcare and future education dimensions among the identified population.ResultsThe proposed model achieved a population identification accuracy of 91.93%, surpassing other baseline neural network algorithms by at least 2.65%. Survey results indicated low satisfaction levels in healthcare areas, including medical resource distribution, medication costs, and access to basic medical facilities, with satisfaction rates below 50%. Regarding future education, issues such as tuition burdens, educational opportunity disparities, and accessibility challenges highlighted the concerns of rural low-income families.DiscussionThe high accuracy of the model demonstrates its potential for precise identification and classification of low-income populations. Insights derived from healthcare and education surveys reveal systemic issues affecting satisfaction and accessibility. This research thus provides a valuable foundation for future studies and policy development targeting rural low-income populations in healthcare and education.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1384474/fullrural low-income populationhealthcarefuture educationtarget population identificationnormalized assistance |
| spellingShingle | Xiaoyan Peng Yanzhao Zeng Yanrui Chen Huaxing Wang Huaxing Wang Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification Frontiers in Public Health rural low-income population healthcare future education target population identification normalized assistance |
| title | Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification |
| title_full | Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification |
| title_fullStr | Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification |
| title_full_unstemmed | Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification |
| title_short | Dual assurance for healthcare and future education development: normalized assistance for low-income population in rural areas—evidence from the population identification |
| title_sort | dual assurance for healthcare and future education development normalized assistance for low income population in rural areas evidence from the population identification |
| topic | rural low-income population healthcare future education target population identification normalized assistance |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1384474/full |
| work_keys_str_mv | AT xiaoyanpeng dualassuranceforhealthcareandfutureeducationdevelopmentnormalizedassistanceforlowincomepopulationinruralareasevidencefromthepopulationidentification AT yanzhaozeng dualassuranceforhealthcareandfutureeducationdevelopmentnormalizedassistanceforlowincomepopulationinruralareasevidencefromthepopulationidentification AT yanruichen dualassuranceforhealthcareandfutureeducationdevelopmentnormalizedassistanceforlowincomepopulationinruralareasevidencefromthepopulationidentification AT huaxingwang dualassuranceforhealthcareandfutureeducationdevelopmentnormalizedassistanceforlowincomepopulationinruralareasevidencefromthepopulationidentification AT huaxingwang dualassuranceforhealthcareandfutureeducationdevelopmentnormalizedassistanceforlowincomepopulationinruralareasevidencefromthepopulationidentification |